Heating, ventilation, and air conditioning system controller

ABSTRACT

Heating, ventilation, and air conditioning (HVAC) controllers are described herein. One method includes receiving an approximate relationship between each of a number of controlled and manipulated variables of an HVAC system, designating one of the number of controlled variables as a primary controlled variable, determining operating parameters for each of the number of manipulated variables that maintain the primary controlled variable based, at least in part, on the approximate relationship between the primary controlled variable and each respective manipulated variable, and determining operating parameters for each of the number of manipulated variables that maintain each of the other controlled variables based, at least in part, on the approximate relationship between each respective other controlled variable and each respective manipulated variable and the determined operating parameters for each of the number of manipulated variables that maintain the primary controlled variable.

PRIORITY INFORMATION

This application is a Continuation of U.S. application Ser. No.14/571,570, filed Dec. 16, 2014, the contents of which are incorporatedherein by reference.

TECHNICAL FIELD

The present disclosure relates to heating, ventilation, and airconditioning system controllers.

BACKGROUND

A heating, ventilation, and air conditioning (HVAC) system can be usedto control the environment of a building. For example, an HVAC systemcan be used to control the air temperature, humidity, and/or air qualityof a building.

A control system (e.g., controller) can be used to control an HVACsystem of a building. For example, the control system (e.g., an operatorof the control system) may attempt to set the operating parameters ofthe manipulated (e.g., input) variables of the HVAC system (e.g., fanspeed, compressor speed, etc.) such that the controlled (e.g., output)variables of the HVAC system (e.g., air temperature, humidity, airquality, power consumption, etc.), and therefore the environment of thebuilding, are maintained at a particular (e.g., desired) set pointand/or within a particular comfort range. However, because the HVACsystem may include multiple manipulated variables, whose operatingparameters may affect multiple controlled variables, it may bedifficult, time consuming, and/or computationally complex to determinethe operating parameters that will maintain the environment of thebuilding at the desired set point and/or within the desired comfortrange.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an HVAC system controller in accordance with one ormore embodiments of the present disclosure.

FIG. 2 illustrates a method for controlling an HVAC system in accordancewith one or more embodiments of the present disclosure.

FIG. 3 illustrates an HVAC system controller in accordance with one ormore embodiments of the present disclosure.

FIG. 4 illustrates a method for controlling an HVAC system in accordancewith one or more embodiments of the present disclosure.

DETAILED DESCRIPTION

Heating, ventilation, and air conditioning (HVAC) system controllers aredescribed herein. For example, one or more embodiments include receivingan approximate relationship between each of a number of controlled andmanipulated variables of an HVAC system, designating one of the numberof controlled variables as a primary controlled variable, determiningoperating parameters for each of the number of manipulated variablesthat maintain the primary controlled variable based, at least in part,on the approximate relationship between the primary controlled variableand each respective manipulated variable, and determining operatingparameters for each of the number of manipulated variables that maintaineach of the other controlled variables based, at least in part, on theapproximate relationship between each respective other controlledvariable and each respective manipulated variable and the determinedoperating parameters for each of the number of manipulated variablesthat maintain the primary controlled variable.

HVAC system controllers in accordance with the present disclosure candetermine operating parameters for an HVAC system (e.g., for themanipulated variables of the HVAC system) that will maintain the HVACsystem (e.g., the controlled variables of the HVAC system) at aparticular (e.g., desired) set point and/or a particular comfort rangein an easier, quicker, and/or less computationally complex manner thanprevious HVAC system controllers. For example, HVAC system controllersin accordance with the present disclosure can be generic, multivariablecontrollers that can determine the operating parameters for multiplemanipulated variables of the HVAC system that maintain multiplecontrolled variables of the HVAC system at a desired set point and/orwithin a desired comfort range in an easier, quicker, and/or lesscomputationally complex manner than previous HVAC system controllers.

For instance, HVAC system controllers in accordance with the presentdisclosure can determine the operating parameters without having to takeinto account system dynamics (e.g., time-varying) and disturbancesassociated with (e.g., acting on) the HVAC system, such as outside airtemperature, people movement within the building, number of people inthe building, occupancy hours of the building, and the fractional loadon the HVAC system, among other dynamic disturbances. Further, HVACsystem controllers in accordance with the present disclosure candetermine the operating parameters without generating or using a model(e.g., the controller can be a non-model based controller). Further, anoperator (e.g., field engineer or technician) of HVAC system controllersin accordance with the present disclosure may not need a high level ofskill or knowledge of the HVAC system in order to use the controller todetermine the operating parameters. Further, HVAC system controllers inaccordance with the present disclosure can have a low computationalfootprint, and can be applicable to and easily embedded within a widerange of currently existing HVAC systems.

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof. The drawings show by wayof illustration how one or more embodiments of the disclosure may bepracticed.

These embodiments are described in sufficient detail to enable those ofordinary skill in the art to practice one or more embodiments of thisdisclosure. It is to be understood that other embodiments may beutilized and that mechanical, electrical, and/or process changes may bemade without departing from the scope of the present disclosure.

As will be appreciated, elements shown in the various embodiments hereincan be added, exchanged, combined, and/or eliminated so as to provide anumber of additional embodiments of the present disclosure. Theproportion and the relative scale of the elements provided in thefigures are intended to illustrate the embodiments of the presentdisclosure, and should not be taken in a limiting sense.

The figures herein follow a numbering convention in which the firstdigit or digits correspond to the drawing figure number and theremaining digits identify an element or component in the drawing.Similar elements or components between different figures may beidentified by the use of similar digits.

As used herein, “a” or “a number of” something can refer to one or moresuch things. For example, “a number of variables” can refer to one ormore variables.

FIG. 1 illustrates a heating, ventilation, and air conditioning (HVAC)system controller 100 in accordance with one or more embodiments of thepresent disclosure. Controller 100 can be used by an operator (e.g.,field engineer or technician) to control, for example, the HVAC systemof a building. That is, controller 100 can be used by the operator tocontrol the environment of the building.

The HVAC system may include a number of components whose operatingparameters can be controlled by controller 100. For example, the HVACsystem may include objects, control components, equipment, devices,networks, sensors, and/or actuators such as, for instance, valves suchas a heating and/or cooling valves, chillers (e.g., chiller plant),boilers (e.g., boiler plant), pumps such as hot water and/or chilledwater pumps, fans, compressors, air dampers such as a variable airvolume (VAV) damper, air handling units (AHUs) (e.g., AHU plant), coilssuch as a heating and/or cooling coil, air filters, and/or coolingtowers, among other components. The HVAC system may also includeconnections (e.g., physical connections) between the components, such asa chain of equipment (e.g., duct work, pipes, ventilation, and/orelectrical and/or gas distribution equipment) that connects thecomponents, among other connections. Further, the HVAC system mayinclude (e.g., be divided into) a number of zones, which can correspondto different zones (e.g., rooms, areas, spaces, and/or floors) of thebuilding.

The HVAC system may have a number of manipulated (e.g., input) variablesand a number of controlled (e.g., output) variables associatedtherewith. The controlled variables can be the variables of the HVACsystem that the operator of controller 100 is attempting to control(e.g., set and/or change), and can correspond to the environmentalconditions of the building. For example, the controlled variables caninclude the air temperature (e.g., ambient and discharge airtemperature), relative humidity, indoor air quality, carbon dioxidelevel, and/or power consumption of the HVAC system (e.g., of the zonesof the HVAC system). The manipulated variables can be the operatingparameters for the components of the HVAC system being controlled bycontroller 100, which can control the controlled variables of the HVACsystem. For example, a typical HVAC system might include manipulatedvariables like the speed of the fans and/or compressors and/or damperposition of the economizer and/or coolant or heating valve position,etc., of the HVAC system. That is, controller 100 can be used to controlthe manipulated variables of the HVAC system, which in turn can controlthe output variables of the HVAC system. For instance, controller 100can determine operating parameters for the manipulated variables thatwill maintain the controlled variables at a particular (e.g., target)set point and/or within a particular comfort range, as will be furtherdescribed herein.

As shown in FIG. 1, controller 100 can include a memory 104 and aprocessor 102. Memory 104 can be any type of storage medium that can beaccessed by processor 102 to perform various examples of the presentdisclosure. For example, memory 104 can be a non-transitory computerreadable medium having computer readable instructions (e.g., computerprogram instructions) stored thereon that are executable by processor102 to determine operating parameters for the manipulated variables ofan HVAC system in accordance with the present disclosure. That is,processor 102 can execute the executable instructions stored in memory104 to determine operating parameters for the manipulated variables ofan HVAC system in accordance with the present disclosure.

Memory 104 can be volatile or nonvolatile memory. Memory 104 can also beremovable (e.g., portable) memory, or non-removable (e.g., internal)memory. For example, memory 104 can be random access memory (RAM) (e.g.,dynamic random access memory (DRAM) and/or phase change random accessmemory (PCRAM)), read-only memory (ROM) (e.g., electrically erasableprogrammable read-only memory (EEPROM) and/or compact-disk read-onlymemory (CD-ROM)), flash memory, a laser disk, a digital versatile disk(DVD) or other optical disk storage, and/or a magnetic medium such asmagnetic cassettes, tapes, or disks, among other types of memory.

Further, although memory 104 is illustrated as being located incontroller 100, embodiments of the present disclosure are not solimited. For example, memory 104 can also be located internal to anothercomputing resource (e.g., enabling computer readable instructions to bedownloaded over the Internet or another wired or wireless connection).

As shown in FIG. 1, controller 100 includes a user interface 106. A user(e.g., operator) of controller 100, such as, for instance, a fieldengineer or technician for the HVAC system controlled by controller 100,can interact with controller 100 via user interface 106. For example,user interface 106 can provide (e.g., display and/or present)information to the user of controller 100, and/or receive informationfrom (e.g., input by) the user of controller 100. For instance, in someembodiments, user interface 106 can be a graphical user interface (GUI)that can include a display (e.g., a screen) that can provide and/orreceive information to and/or from the user of controller 100. Thedisplay can be, for instance, a touch-screen (e.g., the GUI can includetouch-screen capabilities). As an additional example, user interface 106can include a keyboard and/or mouse the user can use to inputinformation into controller 100.

Embodiments of the present disclosure, however, are not limited to aparticular type(s) of user interface.

FIG. 2 illustrates a method 210 for controlling an HVAC system inaccordance with one or more embodiments of the present disclosure. TheHVAC system can be, for example, the HVAC system previously described inconnection with FIG. 1, and method 210 can be performed by, for example,HVAC system controller 100 previously described in connection withFIG. 1. Method 210 can be used to determine operating parameters for theHVAC system (e.g., for the manipulated variables of the HVAC system).

At block 212, method 210 includes receiving user inputs. In someembodiments, the user inputs can be received from the user of thecontroller via the user interface of the controller. In someembodiments, the inputs can be programmed into the controller through agraphical or text-based programming tool.

The received inputs can include, for example, an indication of whetherthere is a positive or negative relationship (e.g., correlation) betweeneach of a number of controlled variables of the HVAC system and each ofa number of manipulated variables of the HVAC system. There may be apositive relationship between a controlled variable and a manipulatedvariable if an increase in the manipulated variable results in anincrease controlled variable, or if a decrease in the manipulatedvariable results in a decrease in the controlled variable. There may bea negative relationship between a controlled variable and a manipulatedvariable if an increase in the manipulated variable results in adecrease controlled variable, or if a decrease in the manipulatedvariable results in an increase in the controlled variable.

The received indication of whether there is a positive or negativerelationship between a controlled variable and a manipulated variablemay not represent the exact or true mathematical relationship betweenthe controlled variable and the manipulated variable. That is, the userdoes not need to know the exact or true mathematical relationshipbetween the controlled variable and the manipulated variable, and hencemay not need a high level of skill or knowledge of the HVAC system.Rather, the received indication may be an indication of an approximaterelationship between the controlled variable and the manipulatedvariable. The relationship may be based on the user's experience and/orknowledge of the HVAC system, and/or the relationship may be based onarchived data associated with the HVAC system.

As an example, there can be two controlled variables (e.g., the airtemperature and the relative humidity of a zone of the HVAC system) andtwo manipulated variables (e.g., the speed of a fan and compressorassociated with the zone) for which the indication relationship arereceived. That is, in such an example, the inputs would include anindication of whether there is a positive or negative relationshipbetween a first controlled variable of the HVAC system and a firstmanipulated variable of the HVAC system, an indication of whether thereis a positive or negative relationship between the first controlledvariable and a second manipulated variable of the HVAC system, anindication of whether there is a positive or negative relationshipbetween a second controlled variable of the HVAC system and the firstmanipulated variable, and an indication of whether there is a positiveor negative relationship between the second controlled variable and thesecond manipulated variable. For instance, the user may indicate thereis a negative relationship between the air temperature and the fanspeed, a negative relationship between the air temperature and thecompressor speed, and a negative relationship between the relativehumidity and the compressor speed, and the user may indicate there is apositive relationship between the relative humidity and the fan speed.Embodiments of the present disclosure, however, are not limited to aparticular number or type of controlled or manipulated variables.

The received inputs can further include a response factor for thecontrolled variables. The response factor can be an indication from theuser as to how fast the controller should determine the operatingparameters for the HVAC system (e.g., the amount of time the controllershould take to determine the operating parameters for the HVAC system).That is, the user can set the speed at which the controller determinesthe operating parameters for the HVAC system using the response factor.

The received inputs can further include operating limits for each of thecontrolled variables and each of the manipulated variables. Theoperating limit for a controlled or manipulated variable can be, forexample, a particular (e.g., target) set point for the variable, or anoperating range (e.g., a minimum and maximum operating level) for thevariable. For instance, in the example referred to above, the operatinglimit for the air temperature may be a set point of 72 degrees, theoperating limit for the relative humidity may be between 40 and 50%, theoperating limit for the fan speed may be between 50 and 100% of themaximum fan speed, and the operating limit for the compressor speed maybe between 25 and 100% of the maximum compressor speed. In someembodiments, the operating limits can be determined as feasible limitsfor the variables based solely on the gain matrix further describedherein.

The received inputs can further include a priority level for each of thecontrolled variables. The priority levels may indicate which controlledvariable(s) is the most important controlled variable(s) to becontrolled for the HVAC system. For example, a priority level of highmay indicate that the controlled variable(s) so designated is the mostimportant controlled variable, while a priority level of low mayindicate that the controlled variable(s) so designated is not asimportant as the other controlled variable(s). For instance, in theexample referred to above, the priority level the air temperature may behigh, while the priority level of the relative humidity may be low. Thatis, it may be more important to control the air temperature than therelative humidity in this example.

At block 214, method 210 includes determining a gain matrix based, atleast in part, on whether there is a positive or negative relationshipbetween each respective controlled variable and each respectivemanipulated variable. Each respective element (e.g., entry) in the gainmatrix can correspond to (e.g., represent) each respective relationshipbetween the controlled and manipulated variables.

For instance, in the example referred to above, the gain matrix may be atwo by two matrix, with the first element corresponding to therelationship between the air temperature and the fan speed, the secondelement corresponding to the relationship between the air temperatureand the compressor speed, the third element corresponding to therelationship between the relative humidity and the fan speed, and thefourth element corresponding to the relationship between the relativehumidity and the compressor speed. That is, the gain matrix G may givenby:

$G = \begin{bmatrix}{- 1} & {- 1} \\1 & {- 1}\end{bmatrix}$That is,

$\begin{bmatrix}y_{1} \\y_{2}\end{bmatrix} = {G\begin{bmatrix}u_{1} \\u_{2}\end{bmatrix}}$where y1 is the air temperature, y2 is the relative humidity, u1 is thefan speed, and u2 is the compressor speed.

At block 216, method 210 includes tuning a particular controlledvariable based, at least in part, on the received inputs (e.g., whetherthere is a positive or negative relationship between that controlledvariable and each respective manipulated variable, the response factor,the operating limits for that controlled variable and the manipulatedvariables, and the priority level for that controlled variable). Thetuning can be done internally by the controller (e.g., without exposingthe tuning process to the user of the controller).

As an example, tuning a particular controlled variable y can includedefining the bounds dy min and dy max for the controlled variable as:dy _(min)=(k ₁ ·de _(min) +k ₂ ·e _(min))/SFdy _(max)=(k ₁ ·de _(max) +k ₂ ·e _(max))/SFwhere:u=k ₁ e+k ₂ ∫edu=k ₁ de+k ₂ edy=Gdu=G(k ₁ de+k ₂ e)and:dy=(k ₁ ·de+k ₂ ·e)/SFe _(min) =y _(min) −ye _(max) =y _(max) −ywhere G is the gain matrix, k1 is the gain tuning factor for thatcontrolled variable, k2 is the integral tuning factor for thatcontrolled variable, and SF is the scale factor for that controlledvariable.

Gain tuning factor k1 and integral tuning factor k2 can be obtained fromstandard PI tuning rules. Gain tuning factor k1 can be a default highvalue, while integral tuning factor k2 can be obtained from a look uptable of k2 values to make the response time of the controller faster orslower. That is, integral tuning factor k2 can be the primary tuningfactor that speeds up the response in the controller determining theoperating parameters for the HVAC system, with higher k2 values makingthe response faster. Scale factor SF will be further described inconnection with block 218 of method 210.

At block 218, method 210 includes verifying (e.g., ensuring) that thereceived operating limits for the tuned controlled variable are possible(e.g., feasible) for the HVAC system by solving a feasibilityoptimization problem. A typical feasibility optimization problem usingpositive and negative relationship can be described as follows.

The objective function is given by:

$\min\limits_{s^{+},s^{-}}\left( {\sum\left\{ {w_{i}\left( {s^{+} + s^{-}} \right)} \right\}} \right)$where w_(i) is a weight for each controlled variable, and s⁻ and s⁺ areslack variables. The constraints can be written as:

-   -   subject to:        0≤s ⁻<∞        0≤s ⁺<∞        (dy _(min))_(k) ≤dy _(k) +s ⁻ −s ⁺≤(dy _(max))_(k)        (u _(k−1) −u _(min))≤du _(k)≤(u _(max) −u _(k−1))        −dy _(k) +Gdu _(k)=0    -   where        (dy _(min))_(k)=(k ₁((e _(min))_(k)−(e _(min))_(k−1)))+k ₂(e        _(min))_(k))/S _(f)        (dy _(max))_(k)=(k ₁((e _(max))_(k)−(e _(max))_(k−1)))+k ₂(e        _(max))_(k))/S _(f)        (e _(min))_(k) =y _(k)−(y _(min))_(k)        (e _(max))_(k)=(y _(max))_(k) −y _(k)

The gain matrix G can be derived from the positive and negativerelationship, and SF is the scale factor, which can be tuned internallyor specified by the user.

As previously described, the response factor RF can be used to inflateor deflate the scale factor SF to make the response faster or slower inthe following way:(s _(F))_(m) =S _(f)/RFdy=(k ₁ ·de+k ₂ ·e)/(s _(f))_(m)e _(min) =y _(min) −ye _(max) =y _(max) −ydy _(min)=(k ₁ ·de _(min) +k ₂ ·e)/(s _(f))_(m)dy _(max)=(k ₁ ·de _(max) +k ₂ ·e)/(s _(f))

At block 220, method 210 includes determining whether all of thecontrolled variables have been tuned. If all of the controlled variableshave been tuned, method 210 proceeds to block 222. If not all of thecontrolled variables have been tuned, method 210 returns to block 216.That is, blocks 216 and 218 are repeated for each respective controlledvariable, until all the controlled variables have been tuned.

At block 222, method 210 includes performing a cost optimization on thetuned controlled variables. The cost optimization can includedetermining operating parameters needed to place (e.g., keep) eachrespective tuned controlled variable within its respective operatinglimit at the lowest possible economic cost based on the gain matrixalone. The lowest possible economic cost can be determined by solvingthe following cost optimization problem:

The objective function is

$\min\limits_{z}z$

-   -   subject to:        −∞≤z≤∞        (dy* _(min))_(k) ≤dy _(k)≤(dy* _(max))_(k)        (dy* _(min))_(k)=(dy _(min))_(k) −Ŝ ⁻(dy* _(max))_(k)=(dy        _(max))_(k) +Ŝ ⁺        (Σ(α·du _(k) +β·dy _(k))−z)=0        −dy _(k) +Gdu _(k)=0    -   Ŝ⁺, Ŝ⁻ solutions from previous step        The gain matrix G is derived from positive and negative        relationship.

At block 224, method 210 includes performing a minimum movementoptimization on the tuned controlled variables. The minimum movementoptimization can include determining operating parameters needed toplace (e.g., keep) each respective tuned controlled variable within itsrespective operating limit with the lowest possible amount of change inthe operating parameters based on the gain matrix information alone. Theminimum movement problem is the solution to the following optimizationproblem:

The objective function is

$\min\limits_{{du}_{k}}\left( {\sum{w_{1_{i}}\left( {du}_{k} \right)}^{2}} \right)$

-   -   subject to:        (dy _(min))_(k) ≤dy _(k)≤(dy _(max))_(k)        (u _(k−1) −u _(min))≤du _(k)≤(u _(max) −u _(k−1))        Σ(α·du _(k) +β·dy _(k))≤z*    -   z* optimal solutions from previous step        −dy _(k) +Gdu _(k)=0        The gain matrix G is derived from positive and negative        relationship.

At block 226, method 210 includes determining (e.g., calculating)operating parameters for each of the manipulated variables of the HVACsystem. The operating parameters for the manipulated variables can bedetermined based, at least in part, on the tuning of the controlledvariables. That is, the operating parameters for the manipulatedvariables can be determined based, at least in part, on the receivedinputs (e.g. whether there is a positive or negative relationshipbetween each respective controlled variable and each respectivemanipulated variable, the response factor, the operating limits for eachrespective controlled variable and manipulated variable, and thepriority level for each respective controlled variable) and the gainmatrix. Further, the operating parameters for the manipulated variablescan be determined based on the cost optimization and the minimummovement optimization performed on the tuned controlled variables. Thedetermined operating parameters for the manipulated variables maymaintain the controlled variables at a pre-determined (e.g., target) setpoint and/or within a pre-determined (e.g., target) comfort range suchas, for instance, the set-point and/or operating range received with theoperating limits.

The determined operating parameters for the manipulated variables,however, may not be based on system dynamics (e.g., time-varying) anddisturbances associated with (e.g., acting on) the HVAC system, such asoutside air temperature, people movement within the building, number ofpeople in the building, occupancy hours of the building, and thefractional load on the HVAC system, among other disturbances. Further,the operating parameters for the manipulated variables may be determinedwithout generating or using a model (e.g., method 210 can be a non-modelbased method). As such, the operating parameters for the manipulatedvariables may be determined in an easier, quicker, and/or lesscomputationally complex manner than in previous approaches.

FIG. 3 illustrates a heating, ventilation, and air conditioning (HVAC)system controller 330 in accordance with one or more embodiments of thepresent disclosure. Controller 330 can be used by an operator (e.g.,field engineer or technician) to control, for example, the HVAC systemof a building. That is, controller 330 can be used by the operator tocontrol the environment of the building.

The HVAC system may be analogous to the HVAC system previously describedherein (e.g., in connection with FIG. 1). For example, the HVAC systemmay include a number of components whose operating parameters can becontrolled by controller 330, in a manner analogous to that previouslydescribed herein. Further, the HVAC system may have a number ofmanipulated (e.g., input) variables and a number of controlled (e.g.,output) variables associated therewith, in a manner analogous to thatpreviously described herein.

As shown in FIG. 3, controller 330 can include a memory 334, a processor332, and a user interface 336. Memory 334, processor 332, and userinterface 336 can be analogous to memory 104, processor 102, and userinterface 106, respectively, previously described in connection withFIG. 1.

As shown in FIG. 3, controller 330 can include a feedback module 338, afeed forward module 342, and reset logic 346. Feedback module 338 caninclude gain matrix 340, and feed forward module 342 can include look uptable 344, as illustrated in FIG. 3. Feedback module 338 (e.g., gainmatrix 340), feed forward module 342 (e.g., look up table 344), andreset logic 346 will be further described herein (e.g., in connectionwith FIG. 4).

As described herein, a “module” can include computer readableinstructions that can be executed by a processing resource to perform aparticular function. A module can also include hardware, firmware,and/or logic that can perform a particular function.

As used herein, “logic” is an alternative or additional processingresource to execute the actions and/or functions, described herein,which includes hardware (e.g., various forms of transistor logic,application specific integrated circuits (ASICs)), as opposed tocomputer executable instructions (e.g., software, firmware) stored inmemory and executable by a processing resource.

FIG. 4 illustrates a method 450 for controlling an HVAC system inaccordance with one or more embodiments of the present disclosure. TheHVAC system can be, for example, the HVAC system previously described inconnection with FIG. 3, and method 450 can be performed by, for example,HVAC system controller 330 previously described in connection with FIG.3. Method 450 can be used to determine operating parameters for the HVACsystem (e.g., for the manipulated variables of the HVAC system).

At block 452, method 450 includes receiving user inputs. In someembodiments, the user inputs can be received from the user of thecontroller via the user interface of the controller. In someembodiments, the inputs can be programmed into the controller through agraphical or text-based programming tool.

The received inputs can include, for example, an approximaterelationship (e.g., correlation) between each of a number of controlledvariables of the HVAC system and each of a number of manipulatedvariables of the HVAC system. That is, the received relationships maynot represent the exact or true mathematical relationships between eachrespective controlled variable and each respective manipulated variable.For instance, the user does not need to know the exact or truemathematical relationship between each respective controlled variableand each respective manipulated variable, and hence may not need a highlevel of skill or knowledge of the HVAC system. The relationship may bebased on the user's experience and/or knowledge of the HVAC system,and/or the relationship may be based on archived data associated withthe HVAC system.

The approximate relationship between a controlled variable of the HVACsystem and a manipulated variable of the HVAC system may include anapproximation of how much and/or how quickly the manipulated variable(e.g., the operating parameters of the manipulated variable) affects(e.g., controls) the controlled variable, and may be represented as anumerical value. For example, the approximate relationship between acontrolled variable and a manipulated variable may be an order ofmagnitude approximation of how much and/or how quickly the manipulatedvariable affects the controlled variable.

As an example, there can be two controlled variables (e.g., the airtemperature and the relative humidity of a zone of the HVAC system) andtwo manipulated variables (e.g., the speed of a fan and compressorassociated with the zone) for which the indication relationship arereceived. That is, in such an example, the inputs would include anapproximate relationship between a first controlled variable of the HVACsystem and a first manipulated variable of the HVAC system, anapproximate relationship between the first controlled variable and asecond manipulated variable of the HVAC system, an approximaterelationship between a second controlled variable of the HVAC system andthe first manipulated variable, and an approximate relationship betweenthe second controlled variable and the second manipulated variable.Embodiments of the present disclosure, however, are not limited to aparticular number or type of controlled or manipulated variables.

The received inputs can further include a designation of one of thecontrolled variables of the HVAC system as a primary controlledvariable, and a designation of the other controlled variables assecondary controlled variables. The designation of a controlled variableas the primary controlled variable can indicate that that controlledvariable has a higher priority level (e.g., is more important tocontrol) in the HVAC system than the controlled variables designated asthe secondary controlled variables. For instance, in the examplereferred to above, the air temperature may be designated as the primarycontrolled variable, while the relative humidity may be designated asthe secondary controlled variable. That is, it may be more important tocontrol the air temperature than the relative humidity in this example.

The received inputs can further include a confidence level for eachrespective approximate relationship. That is, the inputs can include aconfidence level for the approximate relationship between the primarycontrolled variable and each respective manipulated variable, and aconfidence level for the approximate relationship between eachrespective controlled variable and each respective manipulated variable.The confidence level for an approximate relationship can be anindication of the accuracy and/or reliability of that relationship(e.g., an indication of how confident the user is in the accuracy and/orreliability of the approximate relationship).

The received inputs can further include a particular (e.g., target) setpoint or a particular (e.g., target) range (e.g., comfort range) foreach respective controlled variable of the HVAC system. That is, theinputs can include a target set point or range for the primarycontrolled variable, and a target set point or range for each respectivesecondary controlled variable. For instance, in the example referred toabove, the inputs may include a target set point for the air temperatureand a target range for the relative humidity.

The received inputs can further include operating conditions (e.g.,disturbances) for the HVAC system such as, for instance, outside airtemperature, people movement within the building, number of people inthe building, and/or occupancy hours of the building. Further, thereceived inputs can include a time constant (e.g., an estimate of adominant time constant, an approximation of a transport delay by thetime constant) for each approximate relationship. That is, the inputscan include a time constant for the approximate relationship between theprimary controlled variable and each respective manipulated variable,and a time constant for the approximate relationship between eachrespective controlled variable and each respective manipulated variable.

The received inputs can further include a response factor for thecontrolled variables. The response factor can be analogous to theresponse factor previously described in connection with FIG. 2.

At block 454, method 450 includes tuning the primary controlled variableto determine (e.g., calculate) operating parameters for each of themanipulated variables of the HVAC system that will maintain the primarycontrolled variable at its particular (e.g., target) set point or withinits particular (e.g., target) range. The tuning (e.g., the determinationof the operating parameters) can be based, at least in part, on thereceived inputs (e.g., the approximate relationship between the primarycontrolled variable and each respective manipulated variable, theconfidence level and/or time constant for each of those approximaterelationships, the target set point or range for the primary controlledvariable, and/or the operating conditions for the HVAC system).

The tuning (e.g., the determination of the operating parameters) can bedone internally by the controller (e.g., without exposing the tuningprocess to the user of the controller). Further, the operatingparameters for the manipulated variables may be determined withoutgenerating or using a model (e.g., method 450 can be a non-model basedmethod). As such, the operating parameters for the manipulated variablesmay be determined in an easier, quicker, and/or less computationallycomplex manner than in previous approaches.

Tuning the primary controlled variable can include, for example,determining a gain matrix (e.g., gain matrix 340 illustrated in FIG. 3)based, at least in part, on the approximate relationship between theprimary controlled variable and each respective manipulated variable,and determining the operating parameters for each of the manipulatedvariables that maintain the primary controlled variable at its targetset point or within its target range based on the gain matrix. Eachrespective element (e.g., entry) in the gain matrix can correspond to(e.g., represent) each respective approximate relationship between theprimary controlled variable and the manipulated variables. For instance,in the example referred to above, the gain matrix may include an elementcorresponding to the approximate relationship between the airtemperature and the fan speed, and an element corresponding to theapproximate relationship between the air temperature and the compressorspeed. The gain matrix can be determined and/or stored by feedbackmodule 338 illustrated in FIG. 3.

Further, the tuning can be done using a look up table (e.g., look uptable 344 illustrated in FIG. 3) that includes operating parameters foreach of the manipulated variables that will maintain the primarycontrolled variable at the target set point or within the target rangefor different operating conditions (e.g., disturbances) for the HVACsystem. For example, the operating parameters for each of themanipulated variables that maintain the primary controlled variable atits target set point or within its target range can be looked up inand/or selected from the operating parameters included in the look uptable. The look up table can be included (e.g., stored) in feed forwardmodule 342 illustrated in FIG. 3.

The look up table (e.g., the operating parameter values in the table)can be determined based, at least in part, on the approximaterelationship between each respective controlled variable and eachrespective manipulated variable, and on the operating conditions for theHVAC system. That is, the approximate relationships can be used tocalculate the look up table. Further, an uncertainty for the look uptable (e.g., for the operating parameter values in the table) can bedetermined based, at least in part, on the range of and/or confidencelevel for the approximate relationship between the primary controlledvariable and each respective manipulated variable, and the operatingparameters for each of the manipulated variables that maintain theprimary controlled variable at its target set point or within its targetrange can be determined based, at least in part, on the determineduncertainty.

The look up table (e.g., the operating parameter values in the table)and/or the gain matrix can be reset to a default value upon the primarycontrolled variable and/or one of the manipulated variables meeting orexceeding a pre-defined threshold. The default value can be based on,for example, previous set points and/or comfort ranges, previouscontrolled and/or manipulated variables, and/or previous operatingconditions (e.g., disturbances) of the HVAC system. The reset can beperformed by reset logic 346 illustrated in FIG. 3.

As an example tuning of a primary controlled variable, a primarycontrolled variable with the assumed structure of gain and two timeconstants can be given by:

${G(s)} = \frac{G_{0}}{\left( {{\tau\; s} + 1} \right)^{2}}$This can be rearranged as:

${G(s)} = {\frac{g_{1}}{\left( \;{s + \omega_{f}} \right)^{2}} = \frac{g_{0}\left( {\omega_{0}^{2} + \omega_{f}^{2}} \right)}{\left( {s + \omega_{f}} \right)^{2}}}$for which:|G(jω ₀)|=g ₀where g0 and ω0 are slow gain parameters, which can be static orestimated from measured data, and ωf is a bandwidth parameter, which canbe related to the response time or expected response of the HVAC system.Nominal tuning Pnom and robust tuning P can then be done, where:

$P_{nom} = \frac{\omega_{f}^{2}}{2g_{1}}$and:

$P = \frac{\omega_{f}^{2}}{2{kg}_{1}}$where k is the confidence level.

At block 456, method 450 includes tuning a particular secondarycontrolled variable to determine (e.g., calculate) operating parametersfor each of the manipulated variables of the HVAC system that willmaintain that secondary controlled variable at its particular (e.g.,target) set point or within its particular (e.g., target) range. Thetuning (e.g., the determination of the operating parameters) can bebased, at least in part, on the received inputs (e.g., the approximaterelationship between the secondary controlled variable and eachrespective manipulated variable, the confidence level and/or timeconstant for each of those approximate relationships, the target setpoint or range for the secondary controlled variable, and/or theoperating conditions for the HVAC system), and on the previouslydetermined operating parameters for each of the manipulated variablesthat maintain the primary controlled variable at its target set point orwithin its target range. The tuning can be done internally by thecontroller and/or without generating or using a model, as previouslydescribed herein.

Tuning the particular secondary controlled variable can include, forexample, updating (e.g., recalculating) the previously determined gainmatrix based, at least in part, on the approximate relationship betweenthe secondary controlled variable and each respective manipulatedvariable, and on the previously determined operating parameters for eachof the manipulated variables that maintain the primary controlledvariable at its target set point or within its target range. Theoperating parameters for each of the manipulated variables that maintainthe secondary controlled variable at its target set point or within itstarget range can then be determined based on the updated gain matrix.Each respective element (e.g., entry) in the updated gain matrix cancorrespond to (e.g., represent) each respective approximate relationshipbetween the secondary controlled variable and the manipulated variables.For instance, in the example referred to above, the updated gain matrixmay include an element corresponding to the approximate relationshipbetween the relative humidity and the fan speed, and an elementcorresponding to the approximate relationship between the relativehumidity and the compressor speed.

Further, the tuning can be done using the look up table, which may alsoinclude operating parameters for each of the manipulated variables thatwill maintain the particular secondary controlled variable at its targetset point or within the target range for different operating conditions(e.g., disturbances) for the HVAC system. For example, the operatingparameters for each of the manipulated variables that maintain thesecondary controlled variable at its target set point or within itstarget range can be looked up in and/or selected from the operatingparameters included in the look up table, in a manner analogous to thatdescribed in connection with the primary controlled variable.

The look up table (e.g., the operating parameter values in the table)can be determined based, at least in part, on the approximaterelationship between the secondary controlled variable and eachrespective manipulated variable. That is, the approximate relationshipscan be used to calculate the look up table. Further, an uncertainty forthe look up table (e.g., for the operating parameter values in thetable) can be determined based, at least in part, on the range of and/orconfidence level for the approximate relationship between the secondarycontrolled variable and each respective manipulated variable, and theoperating parameters for each of the manipulated variables that maintainthe secondary controlled variable at its target set point or within itstarget range can be determined based, at least in part, on thedetermined uncertainty.

The look up table (e.g., the operating parameter values in the table)and/or the gain matrix can be reset to a default value upon thesecondary controlled variable and/or one of the manipulated variablesmeeting or exceeding a pre-defined threshold, in a manner analogous tothat described in connection with the primary controlled variable.

As an example tuning of a secondary controlled variable, with the sameassumed structure of gain and two time constants as the primarycontrolled variable, the apparent gain can be given by:

$\frac{g_{21}^{\prime}}{g_{21}} = {1 - {\frac{\omega_{f\; 11}}{w_{f\; 11} + \omega_{f\; 12}} \cdot \left( {\frac{g_{22}}{g_{21}} - \frac{g_{12}}{g_{11}}} \right)}}$and tuning P can be done where:

$P = \frac{\omega_{f}^{2}}{2{k_{g}^{\prime}}_{21}}$

At block 458, method 450 includes determining whether all secondarycontrolled variables have been tuned. If all secondary controlledvariables have been tuned, method 450 proceeds to block 460. If not allsecondary controlled variables have been tuned, method 450 returns toblock 456. That is, block 456 is repeated for each respective secondarycontrolled variable, until all the secondary controlled variables havebeen tuned (e.g., the operating parameters for each of the manipulatedvariables that maintain each of the secondary controlled variables aredetermined one secondary controlled variable at a time after theoperating parameters that maintain the primary controlled variable havebeen determined).

At block 460, method 450 includes verifying (e.g., validating) that thedetermined operating parameters for the manipulated variables thatmaintain the primary controlled variable and the secondary controlledvariable(s) at their respective set points or within their respectiveranges are possible (e.g., feasible) for the HVAC system.

Although specific embodiments have been illustrated and describedherein, those of ordinary skill in the art will appreciate that anyarrangement calculated to achieve the same techniques can be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments of thedisclosure.

It is to be understood that the above description has been made in anillustrative fashion, and not a restrictive one. Combination of theabove embodiments, and other embodiments not specifically describedherein will be apparent to those of skill in the art upon reviewing theabove description.

The scope of the various embodiments of the disclosure includes anyother applications in which the above structures and methods are used.Therefore, the scope of various embodiments of the disclosure should bedetermined with reference to the appended claims, along with the fullrange of equivalents to which such claims are entitled.

In the foregoing Detailed Description, various features are groupedtogether in example embodiments illustrated in the figures for thepurpose of streamlining the disclosure. This method of disclosure is notto be interpreted as reflecting an intention that the embodiments of thedisclosure require more features than are expressly recited in eachclaim.

Rather, as the following claims reflect, inventive subject matter liesin less than all features of a single disclosed embodiment. Thus, thefollowing claims are hereby incorporated into the Detailed Description,with each claim standing on its own as a separate embodiment.

What is claimed:
 1. A controller of a heating, ventilation, and airconditioning (HVAC) system, comprising: a user interface configured toreceive: an approximate relationship between each of a number ofcontrolled variables of an HVAC system and each of a number ofmanipulated variables of the HVAC system, wherein the approximaterelationship between each of the number of controlled variables and eachof the number of manipulated variables are order of magnitudeapproximations of the relationship between each respective controlledvariable and each respective manipulated variable; and a designation ofone of the number of controlled variables as a primary controlledvariable and the other controlled variables as secondary controlledvariables; a memory; and a processor configured to execute executableinstructions stored in the memory to: determine operating parameters foreach of the number of manipulated variables that maintain the primarycontrolled variable at a particular set point or within a particularrange based, at least in part, on the approximate relationship betweenthe primary controlled variable and each respective manipulatedvariable; and determine operating parameters for each of the number ofmanipulated variables that maintain each of the secondary controlledvariables at a particular set point or within a particular range based,at least in part, on: the approximate relationship between eachrespective secondary controlled variable and each respective manipulatedvariable; and the determined operating parameters for each of the numberof manipulated variables that maintain the primary controlled variable;wherein the controller is used to control the manipulated variableswhich in turn control the controlled variables.
 2. The controller ofclaim 1, wherein the processor is configured to execute the instructionsto maintain an environment of a building at a particular set point or aparticular range based on the determined operating parameters.
 3. Thecontroller of claim 1, wherein the approximate relationship between eachof the number of controlled variables and each of the number ofmanipulated variables includes an approximation of how much and howquickly each respective manipulated variable affects each respectivecontrolled variable.
 4. The controller of claim 1, wherein the order ofmagnitude approximations of the relationship between each respectivecontrolled variable and each respective manipulated variable arerepresented as numerical values.
 5. The controller of claim 1, wherein:the number of controlled variables comprises two controlled variables;and the number of manipulated variables comprises two manipulatedvariables.
 6. The controller of claim 1, wherein: the user interface isconfigured to receive a response factor for the number of controlledvariables; and the processor is configured to execute the instructionsto: determine the operating parameters for each of the number ofmanipulated variables that maintain the primary controlled variablebased, at least in part, on the response factor; and determine theoperating parameters for each of the number of manipulated variablesthat maintain each of the secondary controlled variables based, at leastin part, on the response factor.
 7. A method for controlling a heating,ventilation, and air conditioning (HVAC) system, comprising: receiving,by a controller of an HVAC system, an approximate relationship betweeneach of a number of controlled variables of the HVAC system and each ofa number of manipulated variables of the HVAC system, wherein theapproximate relationship between each of the number of controlledvariables and each of the number of manipulated variables includes anapproximation of how much each respective manipulated variable affectseach respective controlled variable; designating, by the controller, oneof the number of controlled variables as a primary controlled variable;determining, by the controller, operating parameters for each of thenumber of manipulated variables that maintain the primary controlledvariable at a particular set point or within a particular range based,at least in part, on the approximate relationship between the primarycontrolled variable and each respective manipulated variable; anddetermining, by the controller, operating parameters for each of thenumber of manipulated variables that maintain each of the othercontrolled variables at a particular set point or within a particularrange based, at least in part, on: the approximate relationship betweeneach respective other controlled variable and each respectivemanipulated variable; and the determined operating parameters for eachof the number of manipulated variables that maintain the primarycontrolled variable; controlling, by the controller, the manipulatedvariables which in turn control the controlled variables.
 8. The methodof claim 7, wherein the approximate relationship between each of thenumber of controlled variables and each of the number of manipulatedvariables includes an approximation of how quickly each respectivemanipulated variable affects each respective controlled variable.
 9. Themethod of claim 7, wherein the method includes receiving, by thecontroller, the approximate relationship between each of the number ofcontrolled variables and each of the number of manipulated variablesfrom a user of the controller.
 10. The method of claim 7, wherein themethod includes determining, by the controller, the approximaterelationship between each of the number of controlled variables and eachof the number of manipulated variables based on archived data associatedwith the HVAC system.
 11. The method of claim 7, wherein: the number ofcontrolled variables include an air temperature of a zone of the HVACsystem; and a relative humidity of the zone; the number of manipulatedvariables include: a speed of a fan associated with the zone; and aspeed of a compressor associated with the zone.
 12. The method of claim11, wherein the air temperature of the zone is designated as the primarycontrolled variable.
 13. The method of claim 7, wherein the methodincludes receiving, by the controller, the particular set point orparticular range for the primary controlled variable and the particularset point or particular range for each of the other controlled variablesfrom a user of the controller.
 14. A non-transitory computer readablemedium having computer readable instructions stored thereon that areexecutable by a processor to: receive an approximate relationshipbetween each of a number of controlled variables of an HVAC system andeach of a number of manipulated variables of the HVAC system, whereinthe approximate relationship between each of the number of controlledvariables and each of the number of manipulated variables includes anapproximation of how much and how quickly each respective manipulatedvariable affects each respective controlled variable; designate one ofthe number of controlled variables as a primary controlled variable;determine operating parameters for each of the number of manipulatedvariables that maintain the primary controlled variable at a particularset point based, at least in part, on the approximate relationshipbetween the primary controlled variable and each respective manipulatedvariable; and determine operating parameters for each of the number ofmanipulated variables that maintain each of the other controlledvariables within a particular range based, at least in part, on: theapproximate relationship between each respective other controlledvariable and each respective manipulated variable; and the determinedoperating parameters for each of the number of manipulated variablesthat maintain the primary controlled variable at the particular setpoint; and control the manipulated variables which in turn control thecontrolled variables.
 15. The computer readable medium of claim 14,wherein the instructions are executable by the processor to: receive anindication of an accuracy of the approximate relationship between eachof the number of controlled variables and each of the number ofmanipulated variables; and determine the operating parameters for eachof the number of manipulated variables that maintain the primarycontrolled variable at the particular set point based, at least in part,on the indication of the accuracy of the approximate relationshipbetween the primary controlled variable and reach respective manipulatedvariable.
 16. The computer readable medium of claim 14, wherein theinstructions are executable by the processor to: receive an indicationof a reliability of the approximate relationship between each of thenumber of controlled variables and each of the number of manipulatedvariables; and determine the operating parameters for each of the numberof manipulated variables that maintain the primary controlled variableat the particular set point based, at least in part, on the indicationof the reliability of the approximate relationship between the primarycontrolled variable and reach respective manipulated variable.
 17. Thecomputer readable medium of claim 14, wherein the approximation of howmuch each respective manipulated variable affects each respectivecontrolled variable is represented as a numerical value.
 18. Thecomputer readable medium of claim 14, wherein the approximation of howquickly each respective manipulated variable affects each respectivecontrolled variable is represented as a numerical value.
 19. Thecomputer readable medium of claim 14, wherein the instructions areexecutable by the processor to: determine the operating parameters foreach of the number of manipulated variables that maintain the primarycontrolled variable based, at least in part, on operating conditions forthe HVAC system; and determine the operating parameters for each of thenumber of manipulated variables that maintain each of the othercontrolled variables based, at least in part, on the operatingconditions for the HVAC system.
 20. The computer readable medium ofclaim 14, wherein the instructions are executable by the processor to:determine the operating parameters for each of the number of manipulatedvariables that maintain the primary controlled variable based, at leastin part, on a time constant for the approximate relationship between theprimary controlled variable and each respective manipulated variable;and determine the operating parameters for each of the number ofmanipulated variables that maintain each of the other controlledvariables based, at least in part, on a time constant for theapproximate relationship between each respective other controlledvariable and each respective manipulated variable.